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Pairwise likelihood estimation of latent autoregressive count models.

Authors :
Pedeli X
Varin C
Source :
Statistical methods in medical research [Stat Methods Med Res] 2020 Nov; Vol. 29 (11), pp. 3278-3293. Date of Electronic Publication: 2020 Jun 14.
Publication Year :
2020

Abstract

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.

Details

Language :
English
ISSN :
1477-0334
Volume :
29
Issue :
11
Database :
MEDLINE
Journal :
Statistical methods in medical research
Publication Type :
Academic Journal
Accession number :
32536253
Full Text :
https://doi.org/10.1177/0962280220924068